Method And Computer System For Assessing Classification Annotations Assigned To DNA Sequences

ABSTRACT

For assessing classification annotations assigned to DNA sequences stored in a reference database, the DNA sequences are grouped by species using established classification schemes. Subsequently, a measure of distance between pairs of DNA sequences is determined by aligning the respective sequences and determining the measure of distance based on a score of similarity between the aligned DNA sequences. Determined are one or more centroid sequences which have the shortest aggregate measure of distance to the other DNA sequences in the respective group (species). Assigned to the DNA sequences as a quantitative confidence level for their classification annotations is in each case the measure of distance between the respective DNA sequence and the centroid sequence. The assessment and rating of the classification annotations with these confidence levels make it possible to provide to a user a quantitative indication of the degree of representativeness of a DNA sequence for a particular species.

FIELD OF THE INVENTION

The present invention relates to a computer-implemented method and acomputer system for assessing classification annotations assigned to DNAsequences. Specifically, the present invention relates to acomputer-implemented method and a computer system for assessingclassification annotations assigned to DNA sequences stored in adatabase.

BACKGROUND OF THE INVENTION

Sequence-based identification of life forms is increasingly used fordiagnostic purposes. Being independent of growth and metabolism, thismethod offers significant advantages over conventional culture-basedtechniques in terms of speed and accuracy. Conserved genes present inall bacteria or fungi are amplified and subsequently sequenced usingautomated sequencing techniques. The sequences obtained are thencompared to references in a database. Thus, even rare, unexpected orunusual isolates can be rapidly identified and classified. Sequenceanalysis can be applied to all conserved genes of all life-forms,particularly to microorganisms such as bacteria and fungi.Sequence-based identification of microorganisms relies on comparison ofthe sample signature sequence to a database containing referencesequences representing all relevant genus and species. It is thereforeimportant that a reference database fulfills the following requirements:

-   1) Accurate sequence: the database contains correct sequences of the    requested target, no sequencing errors, no reading flaws, no    artificial gaps, insertions, no vector sequences.-   2) Correct classification annotation (i.e. naming of entries):    sequences are correctly annotated (e.g. species names) and this    information is updated with regard to changes in taxonomy.-   3) Representative: the database represents all relevant life-forms,    e.g. genus and species, including their genetic variants    (intra-species, intra-genomic).-   4) Up-to-date: the references are up-to-date with regard to recently    described species and potential changes in taxonomy (see also 2).

Currently there is no single reference database which fulfils all theserequirements. However, because the quality of results of sequencecomparisons greatly depends on the available references, it is crucialthat these databases be as reliable as possible. In general, scientistsadd entries to public repositories which are of fair quality in terms ofsequence content and annotation (e.g. species name). Nevertheless, thereare many sequencing errors or incorrect annotations with regard tocurrent taxonomy. Annotation errors occur, for example, when sequencesare submitted along with incorrect information about the organism orgene from which the sequence has been derived, or with species nameswhich are not up-to-date (e.g. when species have been reclassifiedtaxonomically, as is often the case for bacteria). When a samplesequence is searched against a reference database, the resulting listusually displays indistinguishably correct and incorrect matches,leaving it up to the expertise of the user to determine references whichwere identified correctly or incorrectly. Thus, a correct sequence withan incorrect annotation could appear on top of the list of matches and,therefore, indicate an erroneous identification of a bacterium, forexample. Because sequence-based pathogen identification is becomingnowadays part of the routine work in medical diagnostic, veterinary andindustry laboratories, there is a need to render sequence databasesearches and comparisons easy and reliable, e.g. for identifying abacterial or fungal species or a virus subtype, or for matching anyunknown organism to a database of well characterized organisms.Particularly, the results of searching and comparing sequence similarityneed to be provided adequately with regard to the expertise of routinelab technicians, who in general do not have a research background orextensive training in bio-informatics or (micro-) organism taxonomy.

US 2007/0083334 describes systems and methods for annotatingbiomolecular sequences. Subsequent to sequence alignment(s),biomolecular sequences are computationally clustered according to aprogressive homology range using one or more clustering algorithms. Abiomolecular sequence is considered to belong to a cluster, if thesequence shares an alignment-based sequence homology above a certainthreshold to one member of the cluster. According to US 2007/0083334,computational clustering can be effected using any commerciallyavailable alignment software including a local homology algorithm. Forexample, a group exhibits a certain degree of homology, if the nucleicacids are 90% identical to one another.

US 2007/0134692 describes an alignment-based method and system forupdating probe array annotation data. One or more clusters are generatedby transcript across datasets retrieved from one or more sources. One ormore probe sequence is aligned to a representative sequence from one ormore of the clusters. The representative sequence is aligned to a genomesequence and the genome sequence is annotated with probe locationinformation. The aligned probe sequences are mapped to the genomesequence using the alignment of the representative sequence and genomesequence. A score is computed using a number associated with the alignedprobe sequences and a number associated with the probe locationformation associated with a region of the genome sequence thatcorresponds to the aligned representative sequence. Redundant entriesmay be eliminated using the clustering method. For example, if thealignment of transcripts in a cluster overlap by >97% over their entirelength, then they are determined to be redundant and only the longestsequence is kept in the cluster.

SUMMARY OF THE INVENTION

It is an object of this invention to provide a computer-implementedmethod and a computer system for assessing (and re-assessing)classification annotations, including taxonomic, systematic and/orfunctional annotations, assigned to DNA sequences. In particular, it isan object of the present invention to provide a computer-implementedmethod and a computer system for assessing qualitatively theclassification annotations such that erroneous and/or doubtfulannotations become easily apparent to lab technicians who do not haveextensive experience or training in bio-informatics or (micro-) organismtaxonomy.

According to the present invention, these objects are achievedparticularly through the features of the independent claims. Inaddition, further advantageous embodiments follow from the dependentclaims and the description.

According to the present invention, the above-mentioned objects areparticularly achieved in that, for assessing classification annotations(including taxonomic, systematic and/or functional annotations) assignedto DNA sequences stored in a database, e.g. a reference database, theDNA sequences are grouped by species using established classificationschemes for taxonomic, systematic and/or functional classification.Subsequently, for pairs of the DNA sequences, determined is in each casea measure of distance between the respective DNA sequences. The measureof distance is determined by aligning automatically the respective DNAsequences and defining the measure of distance based on a score ofsimilarity between the aligned DNA sequences. For example, the measureof distance between two DNA sequences is calculated as a complementaryvalue to the score of similarity, e.g. by subtracting a weighted scoreof similarity from one. For example, the weighted score of similarity iscalculated by dividing the score of similarity between the two DNAsequences through the smaller length of the two DNA sequences.Subsequently, determined is a centroid sequence having the shortestaggregate measure of distance to the DNA sequences. Preferably, within adefined group of DNA sequences, e.g. DNA sequences related to onespecies, the centroid sequence is the one of these DNA sequences thathas the shortest accumulated measure of distance to the other DNAsequences in the group. Alternatively, the centroid sequence is anentirely virtual object, calculated to have the lowest average measureof distance to all the DNA sequences to be considered. It should benoted that within the present context, the term “centroid sequence” isused to include a centroid object representative of an actual DNAsequence as well as a centroid object representative of a virtualobject. Assigned to each one of the DNA sequences to be considered isthe measure of distance between the respective one of the DNA sequencesand the centroid sequence, as a quantitative confidence level for theclassification annotation of the respective one of the DNA sequences.Preferably, the confidence levels are stored in the database assigned tothe respective annotation and DNA sequence which match a known speciesor genus name. The assessment and rating of the classificationannotations with these confidence levels makes it possible to provide toa user an indication of the degree of representativeness of a DNAsequence for a particular species. For example, when a user performs aquery on the database, with each entry in the list of matching referencesequences a field is displayed for the user, indicating the level ofconfidence that the respective DNA sequence is representative for thatparticular species and/or genus. Depending on the embodiment, thequantitative confidence level, i.e. the measure of distance to acentroid sequence, is a numeric value or a qualitatively descriptivevalue derived from the numeric value. For numeric confidence levels, asmall measure of distance indicates a trustworthy annotation, whereaswith a greater distance, the entry should be considered more carefullywith regards to providing a valid identification.

In a preferred embodiment, the measure of distance is determined betweenDNA sequences within a species and centroid sequences are determined forthe DNA sequences within each of the species. Furthermore, outliers aredefined within the species, whereby the outliers are those DNA sequencesthat have the greatest measures of distance to the centroid sequence ofthe respective species. For example, one or more outliers are definedbased on a maximum distance threshold, a defined deviation from anaverage measure of distance, or a defined number or quantity of DNAsequences having the largest measure of distance from the centroidsequence. For outliers which have a smaller measure of distance to acentroid sequence of another species, the annotations are marked asincorrect, e.g. by setting a respective indicator in the database.

In an embodiment, an edge-weighted graph is generated from the scores ofsimilarity between the DNA sequences. In this graph, the DNA sequencesare nodes in the graph, and the nodes are connected, if the score ofsimilarity between the respective DNA sequences is positive (unalignableand dissimilar sequences are assigned a similarity of zero). The measureof distance between the respective DNA sequences is assigned in eachcase an edge weight. For the nodes in the graph, local connectivitydensities (number of connections to other nodes) are computed. Clustersof nodes are defined through progressive aggregation to localconnectivity density maxima, whereby the measure of distance between DNAsequences associated with nodes within a cluster (intra-clusterdistance) is significantly shorter than an average measure of distancebetween the DNA sequences associated with the nodes of the graph(average graph distance).

In a further embodiment, a cluster threshold is received in the computerfrom the user, e.g. in response to the user viewing the graph shown on adisplay. Subsequently, the clusters of nodes are defined by applying thecluster threshold as a maximum intra-cluster distance. Thus, nodesassociated with DNA sequences having a measure of distance greater thanthe maximum intra-cluster distance are not included in the cluster.After application of the cluster threshold, the graph is shown on thedisplay. By selecting different cluster thresholds, the user is enabledto select a level of granularity of the graph in the sense that with arelatively high value of the cluster threshold, the graph is typically acoherent structure connecting all nodes, whereas for smaller clusterthresholds, the graph typically disintegrates into multiple clusters.

Preferably, in the graph-based approach, the DNA sequence associatedwith the node having the highest connectivity density in a cluster, i.e.the highest number of connections to other nodes, is defined thecentroid sequence of that cluster.

In an embodiment, the classification annotation associated with acentroid sequence is assigned to DNA sequences associated with thatcentroid sequence. Specifically, the annotation of the centroid of aparticular cluster is assigned to DNA sequences associated with thenodes of that cluster. Preferably, this annotation does not overwritethe existing classification annotation of a DNA sequence but is added asa recommendation which can be displayed to users.

In addition to a computer-implemented method and a computer system forassessing classification annotations assigned to DNA sequences stored ina database, the present invention also relates to a computer programproduct including computer program code means for controlling one ormore processors of a computer, such that the computer performs themethod, particularly, a computer program product including a computerreadable medium containing therein the computer program code means.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be explained in more detail, by way ofexample, with reference to the drawings in which:

FIG. 1 shows a block diagram illustrating schematically an exemplaryconfiguration of a computer-based system for practicing embodiments ofthe present invention, said configuration comprising a computer systemwith a database, and said configuration being connected to a data entryterminal via a telecommunications network.

FIG. 2 shows a flow diagram illustrating an exemplary sequence of stepsfor rating classification annotations assigned to DNA sequences.

FIG. 3 shows a flow diagram illustrating an exemplary sequence of stepsfor determining one or more centroid sequences.

FIG. 4 shows an example of a cluster of DNA sequences related to acentroid sequence.

FIG. 5 shows an alignment of 11 exemplary variations of DNA sequencesrelated to a species.

FIG. 6 shows an example of a user interface showing to a user possiblematches for a sample sequence, each possible match being indicated witha confidence level (dist).

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In FIG. 1, reference numeral 3 refers to a data entry terminal. Asillustrated in FIG. 1, the data entry terminal 1 includes a personalcomputer 31 with a keyboard 32 and a display monitor 33, for example.

As is illustrated in FIG. 1, the data entry terminal 3 is connected tocomputer system 1 through telecommunications network 2. Preferably, thetelecommunications network 2 includes the Internet and/or an Intranet,making computer system 1 accessible as a web server through the WorldWide Web or within a separate IP-network, respectively.Telecommunications network 2 may also include another fixed network,such as a local area network (LAN) or an integrated services digitalnetwork (ISDN), and/or a wireless network, such as a mobile radionetwork (e.g. Global System for Mobile communication (GSM) or UniversalMobile Telephone System (UMTS)), or a wireless local area network(WLAN). In a variant, at least one data entry terminal 3 is connecteddirectly to computer system 1.

Computer system 1 includes one or more computers, each having one ormore processors. Moreover, the computer system 1 comprises a (reference)database 11 including stored entries of reference DNA sequences 111. Asillustrated schematically in FIG. 1, computer system 1 includesdifferent functional modules, namely a communication module 120, anapplication module 121, a comparator module 122, a centroid detector123, a rating module 124, an error detector 125, and a graph generator126. Database 11 is implemented on a computer shared with the functionalmodules or on a separate computer. As is illustrated schematically inFIG. 1, reference database 11 includes classification annotations 112,including taxonomic, systematic and/or functional annotations,associated with DNA sequences 111. Typically, the content of referencedatabase 11 includes entries related to DNA sequences retrieved andobtained from different (public or private) DNA sequence databases. Thecommunication module 120 includes conventional hardware and softwareelements configured for exchanging data via telecommunications network 2with one or more data entry terminals 3. The application module 121 is aprogrammed software module configured to provide users of the data entryterminal 3 with a user interface 1211. Preferably, user interface 1211is provided through a conventional Internet browser such as MicrosoftExplorer or Mozilla Firefox. The comparator module 122, the centroiddetector 123, the rating module 124, the error detector 125, and thegraph generator 126 are preferably programmed software modules executingon a processor of computer system 1.

Reference numeral 7 refers to a (networked) classification schemedatabase accessible to computer system 1 via telecommunications network2. The classification scheme database includes current establishedclassification schemes for the taxonomic, systematic and/or functionalclassification of DNA sequences of life forms. The classificationschemes are non-static and subject to change and/or addition.

In the following paragraphs the functionality of the functional modulesis described with reference to FIGS. 2 and 3.

In step S1, based on their respective classification annotations 112,the comparator module 122 groups by species the DNA sequences 111 storedin reference database 11 using current established classificationschemes available from the classification scheme database 7. Thegrouping of the DNA sequences is performed for all the DNA sequences 111or for a selected group of the DNA sequences 111. For example, thecomparator module 122 is activated by an operator command a userrequest. In an embodiment, the comparator module 122 is activatedperiodically or automatically whenever a change, addition or updateoccurred to the classification scheme 7, or a defined number of new DNAsequences 111 have been entered (added) in the reference database 11and/or associated with a species. Consequently, the classificationannotations 112 assigned to DNA sequences 111 are assessed andre-assessed continuously and repeatedly, e.g. depending on changes inthe reference database 11 and/or the classification scheme database 7.

In step S2, the comparator module 122 generates a matrix for comparingthe (selected) DNA sequences 111. Depending on the embodiments, onecommon matrix is generated for all the DNA sequences 111, or differentmatrices are generated for each species.

In step S3, the comparator module 122 compares the (selected) DNAsequences 111. First the respective DNA sequences are alignedautomatically in step S31.

FIG. 5 shows an example of an alignment of eleven sequences (e.g.bacterial ribosomal sequences, commonly used for bacterialsequence-based species identification and taxonomy) representing“Abiotrophia defectiva”. As can be seen in FIG. 5, these sequences arenot identical; they carry differences or mutations which may eitherreflect sequencing errors or reflect true intraspecies or intragenomicvariations. From the alignment of these sequences, it becomes apparentthat these variations are often grouped and that it is possible todetermine a sequence which represents best the alignment (here AY879307)and, therefore, also the bacterial species with the annotation“Abiotrophia defectiva”, with regard to all published “Abiotrophiadefectiva” 16S rDNA sequences that are considered.

In step S32, the comparator module 122 determines a score of similaritybetween the aligned DNA sequences 111, e.g. a score expressed as apercentage of sequence correspondence. The scores of similarity betweenthe (selected) DNA sequences 111 are stored in the matrix. It must beemphasized that the score of similarity may be determined using variousdifferent alignment algorithms, e.g. pair wise, global, local, weightedand/or profile-based alignment algorithms, and taking into considerationother elements from the annotations than the classification information.

In step S4, centroid sequence(s) C are determined for the (selected) DNAsequences 111. First, in step S41, the comparator module 122 determinesa measure of distance between the respective (selected) DNA sequences111. The measure of distance is determined based on the scores ofsimilarity between the aligned DNA sequences 111. In an embodiment, themeasure of distance is determined between DNA sequences 111 within aspecies. Preferably, the measures of distance between the (selected) DNAsequences 111 are stored in the matrix.

For example, the measure of distance dist(x, y) between two DNAsequences x and y is calculated by determining a complementary value ofthe score of similarity, e.g. dist(x,y)=1−score(x,y). Preferably, themeasure of distance dist(x, y) between two DNA sequences x and y iscalculated by determining a complementary value of a weighted score ofsimilarity e.g. by subtracting the weighted score of similarity fromone, the weighted score of similarity being calculated by dividing thescore of similarity between the two aligned DNA sequences x, y throughthe smaller length I_(x), I_(y) of the two DNA sequences x, y:

${{dist}\left( {x,y} \right)} = {1 - {\frac{{score}\left( {x,y} \right)}{\min \left( {l_{x},l_{y}} \right)}.}}$

In step S42, based on the measures of distance, the centroid detector123 determines the centroid sequence(s) C for the (selected) DNAsequences 111. Essentially, for each of the grouped species, thecentroid sequence C is the DNA sequence in the group which has theshortest aggregate measure of distance D to the other DNA sequences inthe group. Alternatively, a centroid sequence C is defined as a virtualobject which is determined to have the shortest possible measure ofdistance to all the DNA sequences in the group. In other words, c is thecentroid sequence of a set of sequences S, if for all N sequences s inset S different from c:

D(c) < D(s), where${D\left( s_{i} \right)} = {\sum\limits_{j = 1}^{N}\; {{{dist}\left( {s_{i},s_{j}} \right)}.}}$

There may be more than one (congruent) centroid sequence C for DNAsequences having identical measures of distance.

FIG. 4 shows an example of ten DNA sequences 50-59, representing“Abiotrophia defectiva” as shown in FIG. 5, with their respectivemeasures of distance dist_(i)(x,y) to the centroid sequence C(“AY879307”).

In step S5, the rating module 124 assigns to the (selected) DNAsequences 111 the measure of distance dist_(i)(x,y) between therespective DNA sequence i and the centroid sequence C as a quantitativeconfidence level for the classification annotation assigned to therespective DNA sequence. The smaller the measure of distance associatedwith a sequence, the higher the likelihood that this particular sequenceis close to the centroid and thus carries its annotation correctly.Thus, a small value of the measure of distance dist_(i)(x,y) indicates ahigh level of confidence; whereas a great value of the measure ofdistance dist_(i)(x,y) indicates a low level of confidence. One skilledin the art will understand, that the level of confidence assigned to the(selected) DNA sequences 111 may alternatively be expressed as acomplimentary quantitative value of the measure of distancedist_(i)(x,y) or as a qualitative confidence value derived from themeasure of distance dist_(i)(x,y), e.g. from a set of verbal attributes(e.g. “very high”, “high”, “medium”, “low”, “very low”) or a set ofcolors.

In optional step S6, the error detector 125 identifies outliers amongthe DNA sequences of a species. Outliers have the greatest measure ofdistance to the centroid sequence C of the respective species. Forexample, in FIG. 4, DNA sequence 59 (“AJ496329”) would be detected as anoutlier. In an embodiment, any DNA sequence having a measure of distanceto the centroid sequence C above a defined threshold or standarddeviation is determined an outlier. In an embodiment, outliers areidentified and removed, before determining the centroid sequences(again).

Subsequently, in step S7, the error detector 125 determines whether ornot a detected outlier has a smaller measure of distance to a centroidsequence of another species. If that is the case, in step S8, theclassification annotation of the outlier is marked as incorrect inreference database 11, e.g. by setting a flag field. In addition, in anembodiment, the classification annotation of the closer centroidsequence is stored assigned to the outlier as a proposed classificationannotation.

In a further optional step S9, aside from outliers, the centroiddetector 123 assigns the classification annotation associated with acentroid sequence C to the DNA sequences 50-58 associated with thatcentroid sequence C.

If a user accesses computer system 1 to search the reference database 11with an uploaded DNA sequence sample, e.g. using sequence data of DNAfragments from a DNA sample from a sequencer 4 or from another source,the user is shown a user interface with a list of possible matches 6 asshown in FIG. 6, for example. As can be seen in FIG. 6, each list entryis provided with its respective measure of distance (dist) to thecentroid C as an indicator of the level of confidence. Typically, thelist is presented with a ranking by similarity and the level ofconfidence is used by a user as a measure of reliability of therespective classification annotation. Furthermore, outliers can bevisually marked in the list, e.g. through highlighting or coloring,selectively shown or hidden from the list, and alternativeclassification annotations having a better confidence level can bedisplayed, e.g. as a proposal of a more suitable classification. Thelevel of confidence values can further be included and displayed in anygroupings, alignments, or ranked lists of DNA sequences as well as inphylogenetic trees, for example.

FIG. 3 shows an exemplary sequence of steps for an extended mode ofdetermining the centroid sequences of the (selected) DNA sequences 111.In essence, step S40 is an alternative or complementary approach to thecentroid detection performed in step S4. Processing of step S40 may betriggered upon user selection or detection of a level of complexity bythe centroid detector 123. The level of complexity may be indicated, forexample, by at least a defined number of DNA sequences which have ameasure of distance therein between exceeding a complexity threshold.

In step S401, using the scores of similarity stored in the matrix, thegraph generator 126 generates an edge-weighted graph 5. The nodes in thegraph are representative of the (selected) DNA sequences C, 50-59.Initially, the nodes are connected, if the score of similarity betweenthe respective DNA sequences is positive, i.e. if it is not zero. Aninitial connectivity threshold may be set for the score of similarity toensure that the nodes form one coherent graph. A measure of distancebetween the respective DNA sequences is assigned in each case as an edgeweight between the respective nodes. The measure of distance iscalculated, for example, as described above in the context of step S41.

In step S402, the graph generator 126 computes the local connectivitydensities for the nodes in the graph. The local connectivity density ofa node is defined by the number of connections to other nodes in thegraph.

In step S403, the graph generator 126 defines clusters of nodes in thegraph. The clusters are defined through progressive aggregation to localconnectivity density maxima in the graph. Essentially, the measure ofdistance between DNA sequences associated with nodes within a clusterare significantly shorter than an average measure of distance betweenthe DNA sequences associated with the nodes of the graph. An initialcluster threshold (allowing a large intra-cluster distance) may bedefined for the measure of distance between DNA sequences associatedwith nodes of a cluster so that the whole graph forms just one cluster.

In step S404, the cluster is shown through user interface 1211 to a useron display 33 of data entry terminal 3.

In step S405, optionally, an alternative value for the cluster thresholdis received through user interface 1211 from the user at the data entryterminal 3. If it is determined in step S406 that a new clusterthreshold was received from the user, the graph generator 126 definesthe clusters in step S403 using the new cluster threshold as a maximumintra-cluster distance. Subsequently, the graph with the newly definedcluster is displayed in step S404. If it is determined in step S406 thatno new cluster threshold was received from the user, processingcontinues in step S407.

In step S407, the centroid detector 123 determines the centroidsequence(s) C for the one or more clusters of the graph. For eachcluster, the centroid detector 123 determines the DNA sequenceassociated with the node having the highest connectivity density in thecluster as the centroid sequence C of that cluster. Subsequentlyprocessing continues in step S5 as described above with reference toFIG. 2.

It should be noted that, in the description, the computer program codehas been associated with specific functional modules and the sequence ofthe steps has been presented in a specific order, one skilled in the artwill understand, however, that the computer program code may bestructured differently and that the order of at least some of the stepscould be altered, without deviating from the scope of the invention. Itshould also be noted that the proposed method and system cannot only beused for off-line assessment of classification annotations in adatabase, but also online (real-time or near real-time), e.g. as afilter for entering the classification annotation for a new DNA sequenceto be added to a database.

1. A computer-implemented method of assessing classification annotationsassigned to DNA sequences stored in a database, the method comprising:grouping the DNA sequences by species using established classificationschemes; determining for pairs of the DNA sequences a measure ofdistance between the respective DNA sequences by aligning automaticallythe respective DNA sequences and determining the measure of distancebased on a score of similarity between the aligned DNA sequences;determining a centroid sequence, the centroid sequence having a shortestaggregate measure of distance to the DNA sequences; and assigning to theDNA sequences the measure of distance between the respective DNAsequence and the centroid sequence as a quantitative confidence levelfor the classification annotation assigned to the respective DNAsequence.
 2. The method according to claim 1, wherein the measure ofdistance is determined between DNA sequences within a species; centroidsequences are determined for the DNA sequences within each of thespecies; and the method further comprises identifying outliers withinthe species, the outliers having a greatest measure of distance to thecentroid sequence of the respective species, and marking annotations asincorrect for outliers which have a smaller measure of distance to acentroid sequence of another species.
 3. The method according to claim1, wherein the method further comprises generating from the scores ofsimilarity between the DNA sequences an edge-weighted graph, the DNAsequences being nodes in the graph, the nodes being connected, if thescore of similarity between the respective DNA sequences is positive,and the measure of distance between the respective DNA sequences beingassigned in each case as an edge weight; computing local connectivitydensities for the nodes in the graph; and defining clusters of nodesthrough progressive aggregation to local connectivity density maxima,the measure of distance between DNA sequences associated with nodeswithin a cluster being significantly shorter than an average measure ofdistance between the DNA sequences associated with the nodes of thegraph.
 4. The method according to claim 3, wherein the method furthercomprises receiving a cluster threshold from a user, responsive toshowing the graph on a display; defining the clusters of nodes byapplying the cluster threshold as a maximum intra-cluster distance; andshowing the graph on the display after applying the cluster threshold.5. The method according to claim 3, wherein the DNA sequence associatedwith the node having the highest connectivity density in a cluster isdefined the centroid sequence of that cluster.
 6. The method accordingto claim 1, wherein the classification annotation associated with acentroid sequence is assigned to DNA sequences associated with thatcentroid sequence.
 7. The method according to claim 1, whereindetermining the measure of distance between two DNA sequences includescalculating a weighted score of similarity by dividing the score ofsimilarity between the two DNA sequences through the smaller length ofthe two DNA sequences, and subtracting the weighted score of similarityfrom one.
 8. A computer system for assessing classification annotationsassigned to DNA sequences, the system comprising: database comprising aplurality of the DNA sequences; a comparator module configured to groupthe DNA sequences by species using established classification schemes,and to determine for pairs of the DNA sequences a measure of distancebetween the respective DNA sequences by aligning automatically therespective DNA sequences and determining the measure of distance basedon a score of similarity between the aligned DNA sequences; a centroiddetector configured to determine a centroid sequence, the centroidsequence having a shortest aggregate measure of distance to the DNAsequences; and a rating module configured to assign to the DNA sequencesthe measure of distance between the respective DNA sequence and thecentroid sequence as a quantitative confidence level for theclassification annotation assigned to the respective DNA sequence. 9.The system according to claim 8, wherein the comparator module isfurther configured to determine the measure of distance between DNAsequences within a species; the centroid detector is further configuredto determine the centroid sequences for the DNA sequences within each ofthe species; and the system further comprises an error detectorconfigured to identify outliers within the species, the outliers havinga greatest measure of distance to the centroid sequence of therespective species, and to mark annotations as incorrect for outlierswhich have a smaller measure of distance to a centroid sequence ofanother species.
 10. The system according to claim 9, wherein the systemfurther comprises a graph generator configured to generate from thescores of similarity between the DNA sequences an edge-weighted graph,the DNA sequences being nodes in the graph, the nodes being connected,if the score of similarity between the respective DNA sequences ispositive, and the measure of distance between the respective DNAsequences being assigned in each case as an edge weight, to computelocal connectivity densities for the nodes in the graph, and to defineclusters of nodes through progressive aggregation to local connectivitydensity maxima, the measure of distance between DNA sequences associatedwith nodes within a cluster being significantly shorter than an averagemeasure of distance between the DNA sequences associated with the nodesof the graph.
 11. The system according to claim 10, wherein the systemfurther comprises a user interface configured to receive a clusterthreshold from a user, responsive to showing the graph on a display; thegraph generator is further configured to define the clusters of nodes byapplying the cluster threshold as an maximum intra-cluster distance, andto show the graph on the display after applying the cluster threshold.12. The system according to claim 10, wherein the centroid detector isfurther configured to define the DNA sequence associated with the nodehaving the highest connectivity density in a cluster as the centroidsequence of that cluster.
 13. The system according to claim 8, whereinthe centroid detector is further configured to assign the classificationannotation associated with a centroid sequence to DNA sequencesassociated with that centroid sequence.
 14. The system according toclaim 8, wherein the comparator module is further configured todetermine the measure of distance between two DNA sequences bysubtracting a weighted score of similarity from one, the weighted scoreof similarity being calculated by dividing the score of similaritybetween the two DNA sequences through the smaller length of the two DNAsequences.
 15. A computer program product comprising computer programcode means for controlling one or more processors of a computer system,such that the computer system performs the method according to claim 1.16. The computer program product according to claim 15, furthercomprising a computer readable medium containing therein the computerprogram code means.